Businesses routinely use business intelligence systems that involve the use of data mining models to make sense of increasingly large volumes of business data including but not limited to, for example, marketing data. A business intelligence system may at times include over a hundred operational data mining models. Business decisions made by business analysts are often based on predictions generated by such data mining models. In some cases, individual business rules are developed by business analysts for implementation based on specific predictions generated by one or more data mining models.
Effectively running a business intelligence system typically involves coordination between at least two different parties, the model administrators and the business analysts. The model administrators may include, but are not limited to, model developers, model experts, and statisticians. The model administrators develop customized data mining models, add new models to the system, monitor model performance, and/or perform model maintenance. The business analysts rely on the data mining models to make business decisions including those involving the design and updating of business rules. Business analysts typically like to be informed of changes and updates to data mining models of relevance to their business area so that they can synchronize their business decisions in accordance with the status of available data mining models.
Some prior art data mining model management systems, such as for example, SAS Enterprise Miner, Microsoft Analysis Services, Oracle Data Mining and Analytics, and Fairlsaac Model Builder provide data mining and model management platforms for model administrators. Such prior art data mining management system fail to create a unified model management framework that provides tools for facilitating interactions between model administrators and business analysts.